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Conference Papers Year : 2021

Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges

Abstract

Computational models of emergent communication in agent populations are currently gaining interest in the machine learning community due to recent advances in Multi-Agent Reinforcement Learning (MARL). Current contributions are however still relatively disconnected from the earlier theoretical and computational literature aiming at understanding how language might have emerged from a prelinguistic substance. The goal of this paper is to position recent MARL contributions within the historical context of language evolution research, as well as to extract from this theoretical and computational background a few challenges for future research.
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hal-03051029 , version 1 (15-01-2021)

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Clément Moulin-Frier, Pierre-Yves Oudeyer. Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges. COMARL AAAI 2020-2021 - Challenges and Opportunities for Multi-Agent Reinforcement Learning, AAAI Spring Symposium Series, Feb 2021, Palo Alto, California / Virtual, United States. ⟨hal-03051029⟩
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